COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
It addresses challenges in emotion recognition for conversational AI, but appears incremental as it builds on existing methods with commonsense enhancements.
The paper tackles utterance-level emotion recognition in conversations by incorporating commonsense knowledge, achieving new state-of-the-art results on four benchmark datasets.
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations, and build upon them to learn interactions between interlocutors participating in a conversation. Current state-of-the-art methods often encounter difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. By learning distinct commonsense representations, COSMIC addresses these challenges and achieves new state-of-the-art results for emotion recognition on four different benchmark conversational datasets. Our code is available at https://github.com/declare-lab/conv-emotion.